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Record W4402276336 · doi:10.1186/s40337-024-01068-y

Peer support for eating disorders in Canada: program user perspectives

2024· article· en· W4402276336 on OpenAlexafffundabout
Andrea LaMarre, Jennifer Couturier, Gina Dimitropoulos, Shaleen Jones, Sonia Kumar, Nicole Obeid, Lori Wozney

Bibliographic record

VenueJournal of Eating Disorders · 2024
Typearticle
Languageen
FieldPsychology
TopicEating Disorders and Behaviors
Canadian institutionsIzaak Walton Killam Health CentreHamilton Health SciencesUniversity of CalgaryMcMaster UniversityChildren's Hospital of Eastern Ontario
FundersPublic Health AgencyPublic Health Agency of Canada
KeywordsEating disordersPsychologyComputer scienceClinical psychology

Abstract

fetched live from OpenAlex

Evidence for the value of peer support for eating disorders (EDs) is growing. Peer support is not a replacement for treatment, but can provide hope for recovery, enhance motivation for treatment, and increase feelings of connectedness and belonging for those experiencing EDs. In this study, we explored peer mentees’ experiences of peer support for EDs to better understand the impact of peer support and its role in the Canadian treatment and support continuum. We conducted semi-structured interviews with 20 people who had received peer support (group, one-on-one, or chat) and conducted a reflexive thematic analysis through a critical realist lens. We developed four themes. Participants described the importance of connecting with others who had “been there,” which helped generate a sense of belonging and connectedness. They also highlighted aspects of the peer support “container” that needed to be in place, such as guidelines for groups and agreements for one-on-one mentoring. When peer support was delivered well, participants described how it helped them help themselves through increasing motivation, communication skills, and other coping skills. Finally, participants described the unique role of peer support within the treatment continuum, as a modality that was accessible, free, and delivered in a way that focused on the whole person. Together, our findings illustrate the value of peer support and considerations for enabling effective and safe peer support delivery. Peer support can provide hope for recovery, help people feel more motivated to engage in treatment, and generate a sense of belonging for those experiencing EDs. In this study, we sought to understand how peer mentees experienced peer support for EDs. We also wanted to know what role(s) they thought peer support served within the Canadian treatment and support continuum. We conducted semi-structured interviews with 20 people who had received peer support (group, one-on-one, or chat). We developed four themes using a qualitative analysis approach called reflexive thematic analysis. Participants described the importance of connecting with others who had “been there.” This helped them feel less isolated and more connected to others with lived experience. They also described several things that needed to be in place for peer support to work, including guidelines for groups and agreements for one-on-one mentoring. If these things were in place, peer support could help people learn to help themselves; for instance, participants shared that they felt more motivated and developed stronger coping and communication skills. In terms of the role of peer support, participants shared that it was an accessible and free option, and one that made them feel seen as a whole person. Together, our findings illustrate the value of peer support and considerations for enabling effective and safe peer support delivery.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.268

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0080.001
Scholarly communication0.0030.001
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.333
Teacher spread0.317 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2024
Admission routes3
Has abstractyes

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